Efficient distributed dynamic graph system

Zaki, Aya; Attia, Mahmoud; Hegazy, Doaa; Amin, Safaa

doi:10.1109/intelcis.2015.7397262

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…For instance, HiNode [33] saves the history of each node using Interval Trees and B-Trees. The authors of [34] introduce materialization techniques and parallel complex graph data processing to reduce the memory usage while the model in [55] involves delta log files in its storage in order to reduce the update time.…”

Section: Distributed Graph Over Serversmentioning

confidence: 99%

Timetable-based Routing in Fixed Schedule Dynamic Networks

Narayanan

Rodríguez

2021

2021 International Conference on Computer Communications and Networks (ICCCN)

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Section: Distributed Graph Over Serversmentioning

confidence: 99%

Timetable-based Routing in Fixed Schedule Dynamic Networks

Narayanan

Rodríguez

2021

2021 International Conference on Computer Communications and Networks (ICCCN)

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“…Recently the research on the storage of dynamic graphs with support for historical queries attracts much attention [19], [20]. The fundamental challenge here is the tradeoff between recreation performance and storage consumption [21].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Snapshot Strategy for Dynamic Graph Storage Systems to Support Historical Queries

Luo¹,

Luo²,

Gui

et al. 2020

IEEE Access

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Large-scale dynamic graphs typically involve big data. Recently a dynamic graph storage system is required to be capable of recreating any historical state to support historical queries. A typical storage solution supporting historical queries is called 'snapshot plus log'. A snapshot records the whole data at a certain moment, while the log file is responsible for saving all the update operations. The historical state is then recreated from the nearest snapshot by redoing or undoing the related update operations saved in the log file. The challenge lies in how to minimize both the number of snapshots and that of redone and undone operations performed in historical state recreation. The traditional system stores snapshots at regular intervals. However, historical states do not share the same frequency of being requested. Therefore, the traditional strategy is very inefficient. This paper proposes a new strategy that determines the timestamps of the snapshots based on the distribution of the historical queries. First, the historical queries are clustered into a given number of groups according to the timestamps of the requested historical states, and the cluster centroids are calculated. Second, the snapshots are created according to the timestamps of the cluster centroids. Since the cluster centroids may change as time goes by, the above process is executed periodically. Experimental results show that with the same storage costs, the snapshot strategy proposed in this paper greatly improves the performance of recreating historical states, leading to at least 70.7% computation reduction in terms of the number of both redone and undone operations. Besides, with the same recreation performance guarantee, it brings nearly 78.9% storage reduction on average in terms of the number of snapshots.

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“…Varieties of this category have been proposed in G * [6], M G * [27], and Kineograph [28], where the vertices are assigned to servers based on their hash-value.…”

Section: E Distributed Graph Over Serversmentioning

confidence: 99%

“…* , a modified model of G * , efficiently solved the update overhead problem [27]. M G * represents the historical changes of every server as represented in the log file category.…”

Section: Gmentioning

confidence: 99%

Comprehensive Survey on Dynamic Graph Models

Zaki¹,

Attia²,

Hegazy³

et al. 2016

ijacsa

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Abstract-Most of the critical real-world networks are continuously changing and evolving with time. Motivated by the growing importance and widespread impact of this type of networks, the dynamic nature of these networks have gained a lot of attention. Because of their intrinsic and special characteristics, these networks are best represented by dynamic graph models. To cope with their evolving nature, the representation model must keep the historical information of the network along with its temporal time. Storing such amount of data, poses many problems from the perspective of dynamic graph data management. This survey provides an in-depth overview on dynamic graph related problems. Novel categorization and classification of the state of the art dynamic graph models are also presented in a systematic and comprehensive way. Finally, we discuss dynamic graph processing including the output representation of its algorithms.

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Efficient distributed dynamic graph system

Cited by 5 publications

References 7 publications

Timetable-based Routing in Fixed Schedule Dynamic Networks

Timetable-based Routing in Fixed Schedule Dynamic Networks

An Efficient Snapshot Strategy for Dynamic Graph Storage Systems to Support Historical Queries

Comprehensive Survey on Dynamic Graph Models

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